As one of the key issues of spacecraft formation flying, formation reconfiguration is an important research field of automatic navigation and control. It has a great significance both theoretically and practically. This dissertation addresses the following formation reconfiguration problems: (1) Modeling of formation reconfiguration; (2) Multi-Trajectory planning for spacecraft; (3) Time-Fuel trajectory planning for spacecraft; (4) Cooperative planning for formation reconfiguration; (5) Planning with fuel-balancing for formation reconfiguration. In the modeling of formation reconfiguration, this dissertation perfects the traditional single-objective model based on its attributes, adding the control precision constraint of thrusters to improve the accuracy of the model and to reduce the control feedback error. Furthermore, the multi-objective model of the problem is proposed based on the characteristics of space missions. The objectives include the transfer time, the total fuel consumption and the fuel-balancing between spacecraft. In the research of multi-trajectory planning, a new planner – EMTP (Evolutionary Multiple Trajectory Planner) is presented. By categorizing feasible trajectories according to their distribution in the space and using a retaining method to record multiple trajectories, the algorithm can generate multiple separated trajectories simultaneously, which are optimal or near optimal. The difference of the desired trajectories can be inputted according to the environment and the requirement of missions. The decoupling of the dynamic model is fully used to simplify the problem, decrease the computing time and improve the diversity of the final trajectory selection. Based on niched evolutionary computation, a new time-fuel trajectory planning algorithm is presented. Combining the concepts of evolutionary computation with problem-specific representation of candidates and genetic operators, the generated trajectories can satisfy various constraints. Equivalence class sharing is introduced to guarantee higher selected probability of better individuals and the diversity of the front. The algorithm can find the time-fuel front of the trajectory planning problem effectively. Multiple Pareto solutions can be generated simultaneously to help the decision maker to select the most appropriate solution. In addition, the cooperative planning problem for formation reconfiguration is addressed. The problem is decomposed into two sub-problems and a hierarchical evolutionary algorithm is proposed. The high-level planner performs global planning while ensuring collision avoidance between spacecraft. The low-level planners design multiple optimal or near optimal trajectories which are separated one from another for each spacecraft by parameterizing the controls in terms of Chebyshev polynomials. In the new planner, not only the coevolution of the trajectory sub-populations, but also the coevolution of the high-level and the low-level planners, is achieved. The distributed structure of formation flying is fully used to perform parallel computations. The approach scales well with the number of spacecraft. Although only the best solution is outputted as the final one, multiple separated trajectories for each spacecraft are generated and can be used as alternatives to ensure the execution of the mission. At last, the spacecraft formation reconfiguration problem is modeled as a multi-objective problem and a niched evolutionary algorithm is presented. The relationship of the transfer time, the fuel consumption and the fuel-balancing are investigated simply. Combining the concepts of evolutionary computation with problem specific knowledge, the approach can evaluate one maneuver plan from the above three objectives point of view and generate multiple Pareto optimal maneuver plans simultaneously. It has a small computation cost and is propitious to the simple evaluation of the transfer time and fuel cost.